---
_id: '13996'
abstract:
- lang: eng
text: We report the observation of an anomalous nonlinear optical response of the
prototypical three-dimensional topological insulator bismuth selenide through
the process of high-order harmonic generation. We find that the generation efficiency
increases as the laser polarization is changed from linear to elliptical, and
it becomes maximum for circular polarization. With the aid of a microscopic theory
and a detailed analysis of the measured spectra, we reveal that such anomalous
enhancement encodes the characteristic topology of the band structure that originates
from the interplay of strong spin–orbit coupling and time-reversal symmetry protection.
The implications are in ultrafast probing of topological phase transitions, light-field
driven dissipationless electronics, and quantum computation.
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Denitsa Rangelova
full_name: Baykusheva, Denitsa Rangelova
id: 71b4d059-2a03-11ee-914d-dfa3beed6530
last_name: Baykusheva
- first_name: Alexis
full_name: Chacón, Alexis
last_name: Chacón
- first_name: Jian
full_name: Lu, Jian
last_name: Lu
- first_name: Trevor P.
full_name: Bailey, Trevor P.
last_name: Bailey
- first_name: Jonathan A.
full_name: Sobota, Jonathan A.
last_name: Sobota
- first_name: Hadas
full_name: Soifer, Hadas
last_name: Soifer
- first_name: Patrick S.
full_name: Kirchmann, Patrick S.
last_name: Kirchmann
- first_name: Costel
full_name: Rotundu, Costel
last_name: Rotundu
- first_name: Ctirad
full_name: Uher, Ctirad
last_name: Uher
- first_name: Tony F.
full_name: Heinz, Tony F.
last_name: Heinz
- first_name: David A.
full_name: Reis, David A.
last_name: Reis
- first_name: Shambhu
full_name: Ghimire, Shambhu
last_name: Ghimire
citation:
ama: Baykusheva DR, Chacón A, Lu J, et al. All-optical probe of three-dimensional
topological insulators based on high-harmonic generation by circularly polarized
laser fields. Nano Letters. 2021;21(21):8970-8978. doi:10.1021/acs.nanolett.1c02145
apa: Baykusheva, D. R., Chacón, A., Lu, J., Bailey, T. P., Sobota, J. A., Soifer,
H., … Ghimire, S. (2021). All-optical probe of three-dimensional topological insulators
based on high-harmonic generation by circularly polarized laser fields. Nano
Letters. American Chemical Society. https://doi.org/10.1021/acs.nanolett.1c02145
chicago: Baykusheva, Denitsa Rangelova, Alexis Chacón, Jian Lu, Trevor P. Bailey,
Jonathan A. Sobota, Hadas Soifer, Patrick S. Kirchmann, et al. “All-Optical Probe
of Three-Dimensional Topological Insulators Based on High-Harmonic Generation
by Circularly Polarized Laser Fields.” Nano Letters. American Chemical
Society, 2021. https://doi.org/10.1021/acs.nanolett.1c02145.
ieee: D. R. Baykusheva et al., “All-optical probe of three-dimensional topological
insulators based on high-harmonic generation by circularly polarized laser fields,”
Nano Letters, vol. 21, no. 21. American Chemical Society, pp. 8970–8978,
2021.
ista: Baykusheva DR, Chacón A, Lu J, Bailey TP, Sobota JA, Soifer H, Kirchmann PS,
Rotundu C, Uher C, Heinz TF, Reis DA, Ghimire S. 2021. All-optical probe of three-dimensional
topological insulators based on high-harmonic generation by circularly polarized
laser fields. Nano Letters. 21(21), 8970–8978.
mla: Baykusheva, Denitsa Rangelova, et al. “All-Optical Probe of Three-Dimensional
Topological Insulators Based on High-Harmonic Generation by Circularly Polarized
Laser Fields.” Nano Letters, vol. 21, no. 21, American Chemical Society,
2021, pp. 8970–78, doi:10.1021/acs.nanolett.1c02145.
short: D.R. Baykusheva, A. Chacón, J. Lu, T.P. Bailey, J.A. Sobota, H. Soifer, P.S.
Kirchmann, C. Rotundu, C. Uher, T.F. Heinz, D.A. Reis, S. Ghimire, Nano Letters
21 (2021) 8970–8978.
date_created: 2023-08-09T13:09:15Z
date_published: 2021-10-22T00:00:00Z
date_updated: 2023-08-22T07:32:00Z
day: '22'
doi: 10.1021/acs.nanolett.1c02145
extern: '1'
external_id:
arxiv:
- '2109.15291'
pmid:
- '34676752'
intvolume: ' 21'
issue: '21'
keyword:
- Mechanical Engineering
- Condensed Matter Physics
- General Materials Science
- General Chemistry
- Bioengineering
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.1021/acs.nanolett.1c02145
month: '10'
oa: 1
oa_version: Published Version
page: 8970-8978
pmid: 1
publication: Nano Letters
publication_identifier:
eissn:
- 1530-6992
issn:
- 1530-6984
publication_status: published
publisher: American Chemical Society
quality_controlled: '1'
scopus_import: '1'
status: public
title: All-optical probe of three-dimensional topological insulators based on high-harmonic
generation by circularly polarized laser fields
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 21
year: '2021'
...
---
_id: '13997'
abstract:
- lang: eng
text: We investigate theoretically the strong-field regime of light-matter interactions
in the topological-insulator class of quantum materials. In particular, we focus
on the process of nonperturbative high-order harmonic generation from the paradigmatic
three-dimensional topological insulator bismuth selenide (Bi2Se3) subjected to
intense midinfrared laser fields. We analyze the contributions from the spin-orbit-coupled
bulk states and the topological surface bands separately and reveal a major difference
in how their harmonic yields depend on the ellipticity of the laser field. Bulk
harmonics show a monotonic decrease in their yield as the ellipticity increases,
in a manner reminiscent of high harmonic generation in gaseous media. However,
the surface contribution exhibits a highly nontrivial dependence, culminating
with a maximum for circularly polarized fields. We attribute the observed anomalous
behavior to (i) the enhanced amplitude and the circular pattern of the interband
dipole and the Berry connections in the vicinity of the Dirac point and (ii) the
influence of the higher-order, hexagonal warping terms in the Hamiltonian, which
are responsible for the hexagonal deformation of the energy surface at higher
momenta. The latter are associated directly with spin-orbit-coupling parameters.
Our results thus establish the sensitivity of strong-field-driven high harmonic
emission to the topology of the band structure as well as to the manifestations
of spin-orbit interaction.
article_number: '023101'
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Denitsa Rangelova
full_name: Baykusheva, Denitsa Rangelova
id: 71b4d059-2a03-11ee-914d-dfa3beed6530
last_name: Baykusheva
- first_name: Alexis
full_name: Chacón, Alexis
last_name: Chacón
- first_name: Dasol
full_name: Kim, Dasol
last_name: Kim
- first_name: Dong Eon
full_name: Kim, Dong Eon
last_name: Kim
- first_name: David A.
full_name: Reis, David A.
last_name: Reis
- first_name: Shambhu
full_name: Ghimire, Shambhu
last_name: Ghimire
citation:
ama: Baykusheva DR, Chacón A, Kim D, Kim DE, Reis DA, Ghimire S. Strong-field physics
in three-dimensional topological insulators. Physical Review A. 2021;103(2).
doi:10.1103/physreva.103.023101
apa: Baykusheva, D. R., Chacón, A., Kim, D., Kim, D. E., Reis, D. A., & Ghimire,
S. (2021). Strong-field physics in three-dimensional topological insulators. Physical
Review A. American Physical Society. https://doi.org/10.1103/physreva.103.023101
chicago: Baykusheva, Denitsa Rangelova, Alexis Chacón, Dasol Kim, Dong Eon Kim,
David A. Reis, and Shambhu Ghimire. “Strong-Field Physics in Three-Dimensional
Topological Insulators.” Physical Review A. American Physical Society,
2021. https://doi.org/10.1103/physreva.103.023101.
ieee: D. R. Baykusheva, A. Chacón, D. Kim, D. E. Kim, D. A. Reis, and S. Ghimire,
“Strong-field physics in three-dimensional topological insulators,” Physical
Review A, vol. 103, no. 2. American Physical Society, 2021.
ista: Baykusheva DR, Chacón A, Kim D, Kim DE, Reis DA, Ghimire S. 2021. Strong-field
physics in three-dimensional topological insulators. Physical Review A. 103(2),
023101.
mla: Baykusheva, Denitsa Rangelova, et al. “Strong-Field Physics in Three-Dimensional
Topological Insulators.” Physical Review A, vol. 103, no. 2, 023101, American
Physical Society, 2021, doi:10.1103/physreva.103.023101.
short: D.R. Baykusheva, A. Chacón, D. Kim, D.E. Kim, D.A. Reis, S. Ghimire, Physical
Review A 103 (2021).
date_created: 2023-08-09T13:09:26Z
date_published: 2021-02-01T00:00:00Z
date_updated: 2023-08-22T07:33:43Z
day: '01'
doi: 10.1103/physreva.103.023101
extern: '1'
external_id:
arxiv:
- '2008.01265'
intvolume: ' 103'
issue: '2'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2008.01265
month: '02'
oa: 1
oa_version: Preprint
publication: Physical Review A
publication_identifier:
eissn:
- 2469-9934
issn:
- 2469-9926
publication_status: published
publisher: American Physical Society
quality_controlled: '1'
scopus_import: '1'
status: public
title: Strong-field physics in three-dimensional topological insulators
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 103
year: '2021'
...
---
_id: '14097'
abstract:
- lang: eng
text: UVEX is a proposed medium class Explorer mission designed to provide crucial
missing capabilities that will address objectives central to a broad range of
modern astrophysics. The UVEX design has two co-aligned wide-field imagers operating
in the FUV and NUV and a powerful broadband medium resolution spectrometer. In
its two-year baseline mission, UVEX will perform a multi-cadence synoptic all-sky
survey 50/100 times deeper than GALEX in the NUV/FUV, cadenced surveys of the
Large and Small Magellanic Clouds, rapid target of opportunity followup, as well
as spectroscopic followup of samples of stars and galaxies. The science program
is built around three pillars. First, UVEX will explore the low-mass, low-metallicity
galaxy frontier through imaging and spectroscopic surveys that will probe key
aspects of the evolution of galaxies by understanding how star formation and stellar
evolution at low metallicities affect the growth and evolution of low-metallicity,
low-mass galaxies in the local universe. Such galaxies contain half the mass in
the local universe, and are analogs for the first galaxies, but observed at distances
that make them accessible to detailed study. Second, UVEX will explore the dynamic
universe through time-domain surveys and prompt spectroscopic followup capability
will probe the environments, energetics, and emission processes in the early aftermaths
of gravitational wave-discovered compact object mergers, discover hot, fast UV
transients, and diagnose the early stages of stellar explosions. Finally, UVEX
will become a key community resource by leaving a large all-sky legacy data set,
enabling a wide range of scientific studies and filling a gap in the new generation
of wide-field, sensitive optical and infrared surveys provided by the Rubin, Euclid,
and Roman observatories. This paper discusses the scientific potential of UVEX,
and the broad scientific program.
article_number: '2111.15608'
article_processing_charge: No
arxiv: 1
author:
- first_name: S. R.
full_name: Kulkarni, S. R.
last_name: Kulkarni
- first_name: Fiona A.
full_name: Harrison, Fiona A.
last_name: Harrison
- first_name: Brian W.
full_name: Grefenstette, Brian W.
last_name: Grefenstette
- first_name: Hannah P.
full_name: Earnshaw, Hannah P.
last_name: Earnshaw
- first_name: Igor
full_name: Andreoni, Igor
last_name: Andreoni
- first_name: Danielle A.
full_name: Berg, Danielle A.
last_name: Berg
- first_name: Joshua S.
full_name: Bloom, Joshua S.
last_name: Bloom
- first_name: S. Bradley
full_name: Cenko, S. Bradley
last_name: Cenko
- first_name: Ryan
full_name: Chornock, Ryan
last_name: Chornock
- first_name: Jessie L.
full_name: Christiansen, Jessie L.
last_name: Christiansen
- first_name: Michael W.
full_name: Coughlin, Michael W.
last_name: Coughlin
- first_name: Alexander Wuollet
full_name: Criswell, Alexander Wuollet
last_name: Criswell
- first_name: Behnam
full_name: Darvish, Behnam
last_name: Darvish
- first_name: Kaustav K.
full_name: Das, Kaustav K.
last_name: Das
- first_name: Kishalay
full_name: De, Kishalay
last_name: De
- first_name: Luc
full_name: Dessart, Luc
last_name: Dessart
- first_name: Don
full_name: Dixon, Don
last_name: Dixon
- first_name: Bas
full_name: Dorsman, Bas
last_name: Dorsman
- first_name: Kareem El-Badry
full_name: Kareem El-Badry, Kareem El-Badry
last_name: Kareem El-Badry
- first_name: Christopher
full_name: Evans, Christopher
last_name: Evans
- first_name: K. E. Saavik
full_name: Ford, K. E. Saavik
last_name: Ford
- first_name: Christoffer
full_name: Fremling, Christoffer
last_name: Fremling
- first_name: Boris T.
full_name: Gansicke, Boris T.
last_name: Gansicke
- first_name: Suvi
full_name: Gezari, Suvi
last_name: Gezari
- first_name: Ylva Louise Linsdotter
full_name: Götberg, Ylva Louise Linsdotter
id: d0648d0c-0f64-11ee-a2e0-dd0faa2e4f7d
last_name: Götberg
orcid: 0000-0002-6960-6911
- first_name: Gregory M.
full_name: Green, Gregory M.
last_name: Green
- first_name: Matthew J.
full_name: Graham, Matthew J.
last_name: Graham
- first_name: Marianne
full_name: Heida, Marianne
last_name: Heida
- first_name: Anna Y. Q.
full_name: Ho, Anna Y. Q.
last_name: Ho
- first_name: Amruta D.
full_name: Jaodand, Amruta D.
last_name: Jaodand
- first_name: Christopher M. Johns-Krull
full_name: Christopher M. Johns-Krull, Christopher M. Johns-Krull
last_name: Christopher M. Johns-Krull
- first_name: Mansi M.
full_name: Kasliwal, Mansi M.
last_name: Kasliwal
- first_name: Margaret
full_name: Lazzarini, Margaret
last_name: Lazzarini
- first_name: Wenbin
full_name: Lu, Wenbin
last_name: Lu
- first_name: Raffaella
full_name: Margutti, Raffaella
last_name: Margutti
- first_name: D. Christopher
full_name: Martin, D. Christopher
last_name: Martin
- first_name: Daniel Charles
full_name: Masters, Daniel Charles
last_name: Masters
- first_name: Barry
full_name: McKernan, Barry
last_name: McKernan
- first_name: Yael
full_name: Naze, Yael
last_name: Naze
- first_name: Samaya M.
full_name: Nissanke, Samaya M.
last_name: Nissanke
- first_name: B.
full_name: Parazin, B.
last_name: Parazin
- first_name: Daniel A.
full_name: Perley, Daniel A.
last_name: Perley
- first_name: E. Sterl
full_name: Phinney, E. Sterl
last_name: Phinney
- first_name: Anthony L.
full_name: Piro, Anthony L.
last_name: Piro
- first_name: G.
full_name: Raaijmakers, G.
last_name: Raaijmakers
- first_name: Gregor
full_name: Rauw, Gregor
last_name: Rauw
- first_name: Antonio C.
full_name: Rodriguez, Antonio C.
last_name: Rodriguez
- first_name: Hugues
full_name: Sana, Hugues
last_name: Sana
- first_name: Peter
full_name: Senchyna, Peter
last_name: Senchyna
- first_name: Leo P.
full_name: Singer, Leo P.
last_name: Singer
- first_name: Jessica J.
full_name: Spake, Jessica J.
last_name: Spake
- first_name: Keivan G.
full_name: Stassun, Keivan G.
last_name: Stassun
- first_name: Daniel
full_name: Stern, Daniel
last_name: Stern
- first_name: Harry I.
full_name: Teplitz, Harry I.
last_name: Teplitz
- first_name: Daniel R.
full_name: Weisz, Daniel R.
last_name: Weisz
- first_name: Yuhan
full_name: Yao, Yuhan
last_name: Yao
citation:
ama: Kulkarni SR, Harrison FA, Grefenstette BW, et al. Science with the ultraviolet
explorer (UVEX). arXiv. doi:10.48550/arXiv.2111.15608
apa: Kulkarni, S. R., Harrison, F. A., Grefenstette, B. W., Earnshaw, H. P., Andreoni,
I., Berg, D. A., … Yao, Y. (n.d.). Science with the ultraviolet explorer (UVEX).
arXiv. https://doi.org/10.48550/arXiv.2111.15608
chicago: Kulkarni, S. R., Fiona A. Harrison, Brian W. Grefenstette, Hannah P. Earnshaw,
Igor Andreoni, Danielle A. Berg, Joshua S. Bloom, et al. “Science with the Ultraviolet
Explorer (UVEX).” ArXiv, n.d. https://doi.org/10.48550/arXiv.2111.15608.
ieee: S. R. Kulkarni et al., “Science with the ultraviolet explorer (UVEX),”
arXiv. .
ista: Kulkarni SR, Harrison FA, Grefenstette BW, Earnshaw HP, Andreoni I, Berg DA,
Bloom JS, Cenko SB, Chornock R, Christiansen JL, Coughlin MW, Criswell AW, Darvish
B, Das KK, De K, Dessart L, Dixon D, Dorsman B, Kareem El-Badry KE-B, Evans C,
Ford KES, Fremling C, Gansicke BT, Gezari S, Götberg YLL, Green GM, Graham MJ,
Heida M, Ho AYQ, Jaodand AD, Christopher M. Johns-Krull CMJ-K, Kasliwal MM, Lazzarini
M, Lu W, Margutti R, Martin DC, Masters DC, McKernan B, Naze Y, Nissanke SM, Parazin
B, Perley DA, Phinney ES, Piro AL, Raaijmakers G, Rauw G, Rodriguez AC, Sana H,
Senchyna P, Singer LP, Spake JJ, Stassun KG, Stern D, Teplitz HI, Weisz DR, Yao
Y. Science with the ultraviolet explorer (UVEX). arXiv, 2111.15608.
mla: Kulkarni, S. R., et al. “Science with the Ultraviolet Explorer (UVEX).” ArXiv,
2111.15608, doi:10.48550/arXiv.2111.15608.
short: S.R. Kulkarni, F.A. Harrison, B.W. Grefenstette, H.P. Earnshaw, I. Andreoni,
D.A. Berg, J.S. Bloom, S.B. Cenko, R. Chornock, J.L. Christiansen, M.W. Coughlin,
A.W. Criswell, B. Darvish, K.K. Das, K. De, L. Dessart, D. Dixon, B. Dorsman,
K.E.-B. Kareem El-Badry, C. Evans, K.E.S. Ford, C. Fremling, B.T. Gansicke, S.
Gezari, Y.L.L. Götberg, G.M. Green, M.J. Graham, M. Heida, A.Y.Q. Ho, A.D. Jaodand,
C.M.J.-K. Christopher M. Johns-Krull, M.M. Kasliwal, M. Lazzarini, W. Lu, R. Margutti,
D.C. Martin, D.C. Masters, B. McKernan, Y. Naze, S.M. Nissanke, B. Parazin, D.A.
Perley, E.S. Phinney, A.L. Piro, G. Raaijmakers, G. Rauw, A.C. Rodriguez, H. Sana,
P. Senchyna, L.P. Singer, J.J. Spake, K.G. Stassun, D. Stern, H.I. Teplitz, D.R.
Weisz, Y. Yao, ArXiv (n.d.).
date_created: 2023-08-21T10:11:00Z
date_published: 2021-11-30T00:00:00Z
date_updated: 2023-08-22T13:15:02Z
day: '30'
doi: 10.48550/arXiv.2111.15608
extern: '1'
external_id:
arxiv:
- '2111.15608'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: ' https://doi.org/10.48550/arXiv.2111.15608'
month: '11'
oa: 1
oa_version: Preprint
publication: arXiv
publication_status: submitted
status: public
title: Science with the ultraviolet explorer (UVEX)
type: preprint
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2021'
...
---
_id: '14117'
abstract:
- lang: eng
text: 'The two fields of machine learning and graphical causality arose and are
developed separately. However, there is, now, cross-pollination and increasing
interest in both fields to benefit from the advances of the other. In this article,
we review fundamental concepts of causal inference and relate them to crucial
open problems of machine learning, including transfer and generalization, thereby
assaying how causality can contribute to modern machine learning research. This
also applies in the opposite direction: we note that most work in causality starts
from the premise that the causal variables are given. A central problem for AI
and causality is, thus, causal representation learning, that is, the discovery
of high-level causal variables from low-level observations. Finally, we delineate
some implications of causality for machine learning and propose key research areas
at the intersection of both communities.'
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Bernhard
full_name: Scholkopf, Bernhard
last_name: Scholkopf
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
- first_name: Stefan
full_name: Bauer, Stefan
last_name: Bauer
- first_name: Nan Rosemary
full_name: Ke, Nan Rosemary
last_name: Ke
- first_name: Nal
full_name: Kalchbrenner, Nal
last_name: Kalchbrenner
- first_name: Anirudh
full_name: Goyal, Anirudh
last_name: Goyal
- first_name: Yoshua
full_name: Bengio, Yoshua
last_name: Bengio
citation:
ama: Scholkopf B, Locatello F, Bauer S, et al. Toward causal representation learning.
Proceedings of the IEEE. 2021;109(5):612-634. doi:10.1109/jproc.2021.3058954
apa: Scholkopf, B., Locatello, F., Bauer, S., Ke, N. R., Kalchbrenner, N., Goyal,
A., & Bengio, Y. (2021). Toward causal representation learning. Proceedings
of the IEEE. Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/jproc.2021.3058954
chicago: Scholkopf, Bernhard, Francesco Locatello, Stefan Bauer, Nan Rosemary Ke,
Nal Kalchbrenner, Anirudh Goyal, and Yoshua Bengio. “Toward Causal Representation
Learning.” Proceedings of the IEEE. Institute of Electrical and Electronics
Engineers, 2021. https://doi.org/10.1109/jproc.2021.3058954.
ieee: B. Scholkopf et al., “Toward causal representation learning,” Proceedings
of the IEEE, vol. 109, no. 5. Institute of Electrical and Electronics Engineers,
pp. 612–634, 2021.
ista: Scholkopf B, Locatello F, Bauer S, Ke NR, Kalchbrenner N, Goyal A, Bengio
Y. 2021. Toward causal representation learning. Proceedings of the IEEE. 109(5),
612–634.
mla: Scholkopf, Bernhard, et al. “Toward Causal Representation Learning.” Proceedings
of the IEEE, vol. 109, no. 5, Institute of Electrical and Electronics Engineers,
2021, pp. 612–34, doi:10.1109/jproc.2021.3058954.
short: B. Scholkopf, F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal,
Y. Bengio, Proceedings of the IEEE 109 (2021) 612–634.
date_created: 2023-08-21T12:19:30Z
date_published: 2021-05-01T00:00:00Z
date_updated: 2023-09-11T11:43:35Z
day: '01'
department:
- _id: FrLo
doi: 10.1109/jproc.2021.3058954
extern: '1'
external_id:
arxiv:
- '2102.11107'
intvolume: ' 109'
issue: '5'
keyword:
- Electrical and Electronic Engineering
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.1109/JPROC.2021.3058954
month: '05'
oa: 1
oa_version: Published Version
page: 612-634
publication: Proceedings of the IEEE
publication_identifier:
eissn:
- 1558-2256
issn:
- 0018-9219
publication_status: published
publisher: Institute of Electrical and Electronics Engineers
quality_controlled: '1'
scopus_import: '1'
status: public
title: Toward causal representation learning
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 109
year: '2021'
...
---
_id: '14176'
abstract:
- lang: eng
text: "Intensive care units (ICU) are increasingly looking towards machine learning
for methods to provide online monitoring of critically ill patients. In machine
learning, online monitoring is often formulated as a supervised learning problem.
Recently, contrastive learning approaches have demonstrated promising improvements
over competitive supervised benchmarks. These methods rely on well-understood
data augmentation techniques developed for image data which do not apply to online
monitoring. In this work, we overcome this limitation by\r\nsupplementing time-series
data augmentation techniques with a novel contrastive\r\nlearning objective which
we call neighborhood contrastive learning (NCL). Our objective explicitly groups
together contiguous time segments from each patient while maintaining state-specific
information. Our experiments demonstrate a marked improvement over existing work
applying contrastive methods to medical time-series."
alternative_title:
- PMLR
article_processing_charge: No
arxiv: 1
author:
- first_name: Hugo
full_name: Yèche, Hugo
last_name: Yèche
- first_name: Gideon
full_name: Dresdner, Gideon
last_name: Dresdner
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
- first_name: Matthias
full_name: Hüser, Matthias
last_name: Hüser
- first_name: Gunnar
full_name: Rätsch, Gunnar
last_name: Rätsch
citation:
ama: 'Yèche H, Dresdner G, Locatello F, Hüser M, Rätsch G. Neighborhood contrastive
learning applied to online patient monitoring. In: Proceedings of 38th International
Conference on Machine Learning. Vol 139. ML Research Press; 2021:11964-11974.'
apa: 'Yèche, H., Dresdner, G., Locatello, F., Hüser, M., & Rätsch, G. (2021).
Neighborhood contrastive learning applied to online patient monitoring. In Proceedings
of 38th International Conference on Machine Learning (Vol. 139, pp. 11964–11974).
Virtual: ML Research Press.'
chicago: Yèche, Hugo, Gideon Dresdner, Francesco Locatello, Matthias Hüser, and
Gunnar Rätsch. “Neighborhood Contrastive Learning Applied to Online Patient Monitoring.”
In Proceedings of 38th International Conference on Machine Learning, 139:11964–74.
ML Research Press, 2021.
ieee: H. Yèche, G. Dresdner, F. Locatello, M. Hüser, and G. Rätsch, “Neighborhood
contrastive learning applied to online patient monitoring,” in Proceedings
of 38th International Conference on Machine Learning, Virtual, 2021, vol.
139, pp. 11964–11974.
ista: Yèche H, Dresdner G, Locatello F, Hüser M, Rätsch G. 2021. Neighborhood contrastive
learning applied to online patient monitoring. Proceedings of 38th International
Conference on Machine Learning. International Conference on Machine Learning,
PMLR, vol. 139, 11964–11974.
mla: Yèche, Hugo, et al. “Neighborhood Contrastive Learning Applied to Online Patient
Monitoring.” Proceedings of 38th International Conference on Machine Learning,
vol. 139, ML Research Press, 2021, pp. 11964–74.
short: H. Yèche, G. Dresdner, F. Locatello, M. Hüser, G. Rätsch, in:, Proceedings
of 38th International Conference on Machine Learning, ML Research Press, 2021,
pp. 11964–11974.
conference:
end_date: 2021-07-24
location: Virtual
name: International Conference on Machine Learning
start_date: 2021-07-18
date_created: 2023-08-22T14:03:04Z
date_published: 2021-08-01T00:00:00Z
date_updated: 2023-09-11T10:16:55Z
day: '01'
department:
- _id: FrLo
extern: '1'
external_id:
arxiv:
- '2106.05142'
intvolume: ' 139'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2106.05142
month: '08'
oa: 1
oa_version: Preprint
page: 11964-11974
publication: Proceedings of 38th International Conference on Machine Learning
publication_status: published
publisher: ML Research Press
quality_controlled: '1'
scopus_import: '1'
status: public
title: Neighborhood contrastive learning applied to online patient monitoring
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 139
year: '2021'
...
---
_id: '14177'
abstract:
- lang: eng
text: "The focus of disentanglement approaches has been on identifying independent
factors of variation in data. However, the causal variables underlying real-world
observations are often not statistically independent. In this work, we bridge
the gap to real-world scenarios by analyzing the behavior of the most prominent
disentanglement approaches on correlated data in a large-scale empirical study
(including 4260 models). We show and quantify that systematically induced correlations
in the dataset are being learned and reflected in the latent representations,
which has implications for downstream applications of disentanglement such as
fairness. We also demonstrate how to resolve these latent correlations, either
using weak supervision during\r\ntraining or by post-hoc correcting a pre-trained
model with a small number of labels."
alternative_title:
- PMLR
article_processing_charge: No
arxiv: 1
author:
- first_name: Frederik
full_name: Träuble, Frederik
last_name: Träuble
- first_name: Elliot
full_name: Creager, Elliot
last_name: Creager
- first_name: Niki
full_name: Kilbertus, Niki
last_name: Kilbertus
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
- first_name: Andrea
full_name: Dittadi, Andrea
last_name: Dittadi
- first_name: Anirudh
full_name: Goyal, Anirudh
last_name: Goyal
- first_name: Bernhard
full_name: Schölkopf, Bernhard
last_name: Schölkopf
- first_name: Stefan
full_name: Bauer, Stefan
last_name: Bauer
citation:
ama: 'Träuble F, Creager E, Kilbertus N, et al. On disentangled representations
learned from correlated data. In: Proceedings of the 38th International Conference
on Machine Learning. Vol 139. ML Research Press; 2021:10401-10412.'
apa: 'Träuble, F., Creager, E., Kilbertus, N., Locatello, F., Dittadi, A., Goyal,
A., … Bauer, S. (2021). On disentangled representations learned from correlated
data. In Proceedings of the 38th International Conference on Machine Learning
(Vol. 139, pp. 10401–10412). Virtual: ML Research Press.'
chicago: Träuble, Frederik, Elliot Creager, Niki Kilbertus, Francesco Locatello,
Andrea Dittadi, Anirudh Goyal, Bernhard Schölkopf, and Stefan Bauer. “On Disentangled
Representations Learned from Correlated Data.” In Proceedings of the 38th International
Conference on Machine Learning, 139:10401–12. ML Research Press, 2021.
ieee: F. Träuble et al., “On disentangled representations learned from correlated
data,” in Proceedings of the 38th International Conference on Machine Learning,
Virtual, 2021, vol. 139, pp. 10401–10412.
ista: 'Träuble F, Creager E, Kilbertus N, Locatello F, Dittadi A, Goyal A, Schölkopf
B, Bauer S. 2021. On disentangled representations learned from correlated data.
Proceedings of the 38th International Conference on Machine Learning. ICML: International
Conference on Machine Learning, PMLR, vol. 139, 10401–10412.'
mla: Träuble, Frederik, et al. “On Disentangled Representations Learned from Correlated
Data.” Proceedings of the 38th International Conference on Machine Learning,
vol. 139, ML Research Press, 2021, pp. 10401–12.
short: F. Träuble, E. Creager, N. Kilbertus, F. Locatello, A. Dittadi, A. Goyal,
B. Schölkopf, S. Bauer, in:, Proceedings of the 38th International Conference
on Machine Learning, ML Research Press, 2021, pp. 10401–10412.
conference:
end_date: 2021-07-24
location: Virtual
name: 'ICML: International Conference on Machine Learning'
start_date: 2021-07-18
date_created: 2023-08-22T14:03:47Z
date_published: 2021-08-01T00:00:00Z
date_updated: 2023-09-11T10:18:48Z
day: '01'
department:
- _id: FrLo
extern: '1'
external_id:
arxiv:
- '2006.07886'
intvolume: ' 139'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2006.07886
month: '08'
oa: 1
oa_version: Published Version
page: 10401-10412
publication: Proceedings of the 38th International Conference on Machine Learning
publication_status: published
publisher: ML Research Press
quality_controlled: '1'
scopus_import: '1'
status: public
title: On disentangled representations learned from correlated data
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 139
year: '2021'
...
---
_id: '14178'
abstract:
- lang: eng
text: Learning meaningful representations that disentangle the underlying structure
of the data generating process is considered to be of key importance in machine
learning. While disentangled representations were found to be useful for diverse
tasks such as abstract reasoning and fair classification, their scalability and
real-world impact remain questionable. We introduce a new high-resolution dataset
with 1M simulated images and over 1,800 annotated real-world images of the same
setup. In contrast to previous work, this new dataset exhibits correlations, a
complex underlying structure, and allows to evaluate transfer to unseen simulated
and real-world settings where the encoder i) remains in distribution or ii) is
out of distribution. We propose new architectures in order to scale disentangled
representation learning to realistic high-resolution settings and conduct a large-scale
empirical study of disentangled representations on this dataset. We observe that
disentanglement is a good predictor for out-of-distribution (OOD) task performance.
article_processing_charge: No
arxiv: 1
author:
- first_name: Andrea
full_name: Dittadi, Andrea
last_name: Dittadi
- first_name: Frederik
full_name: Träuble, Frederik
last_name: Träuble
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
- first_name: Manuel
full_name: Wüthrich, Manuel
last_name: Wüthrich
- first_name: Vaibhav
full_name: Agrawal, Vaibhav
last_name: Agrawal
- first_name: Ole
full_name: Winther, Ole
last_name: Winther
- first_name: Stefan
full_name: Bauer, Stefan
last_name: Bauer
- first_name: Bernhard
full_name: Schölkopf, Bernhard
last_name: Schölkopf
citation:
ama: 'Dittadi A, Träuble F, Locatello F, et al. On the transfer of disentangled
representations in realistic settings. In: The Ninth International Conference
on Learning Representations. ; 2021.'
apa: Dittadi, A., Träuble, F., Locatello, F., Wüthrich, M., Agrawal, V., Winther,
O., … Schölkopf, B. (2021). On the transfer of disentangled representations in
realistic settings. In The Ninth International Conference on Learning Representations.
Virtual.
chicago: Dittadi, Andrea, Frederik Träuble, Francesco Locatello, Manuel Wüthrich,
Vaibhav Agrawal, Ole Winther, Stefan Bauer, and Bernhard Schölkopf. “On the Transfer
of Disentangled Representations in Realistic Settings.” In The Ninth International
Conference on Learning Representations, 2021.
ieee: A. Dittadi et al., “On the transfer of disentangled representations
in realistic settings,” in The Ninth International Conference on Learning Representations,
Virtual, 2021.
ista: 'Dittadi A, Träuble F, Locatello F, Wüthrich M, Agrawal V, Winther O, Bauer
S, Schölkopf B. 2021. On the transfer of disentangled representations in realistic
settings. The Ninth International Conference on Learning Representations. ICLR:
International Conference on Learning Representations.'
mla: Dittadi, Andrea, et al. “On the Transfer of Disentangled Representations in
Realistic Settings.” The Ninth International Conference on Learning Representations,
2021.
short: A. Dittadi, F. Träuble, F. Locatello, M. Wüthrich, V. Agrawal, O. Winther,
S. Bauer, B. Schölkopf, in:, The Ninth International Conference on Learning Representations,
2021.
conference:
end_date: 2021-05-07
location: Virtual
name: 'ICLR: International Conference on Learning Representations'
start_date: 2021-05-03
date_created: 2023-08-22T14:04:16Z
date_published: 2021-05-04T00:00:00Z
date_updated: 2023-09-11T10:55:30Z
day: '04'
department:
- _id: FrLo
extern: '1'
external_id:
arxiv:
- '2010.14407'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2010.14407
month: '05'
oa: 1
oa_version: Preprint
publication: The Ninth International Conference on Learning Representations
publication_status: published
quality_controlled: '1'
status: public
title: On the transfer of disentangled representations in realistic settings
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2021'
...
---
_id: '14179'
abstract:
- lang: eng
text: Self-supervised representation learning has shown remarkable success in a
number of domains. A common practice is to perform data augmentation via hand-crafted
transformations intended to leave the semantics of the data invariant. We seek
to understand the empirical success of this approach from a theoretical perspective.
We formulate the augmentation process as a latent variable model by postulating
a partition of the latent representation into a content component, which is assumed
invariant to augmentation, and a style component, which is allowed to change.
Unlike prior work on disentanglement and independent component analysis, we allow
for both nontrivial statistical and causal dependencies in the latent space. We
study the identifiability of the latent representation based on pairs of views
of the observations and prove sufficient conditions that allow us to identify
the invariant content partition up to an invertible mapping in both generative
and discriminative settings. We find numerical simulations with dependent latent
variables are consistent with our theory. Lastly, we introduce Causal3DIdent,
a dataset of high-dimensional, visually complex images with rich causal dependencies,
which we use to study the effect of data augmentations performed in practice.
article_processing_charge: No
arxiv: 1
author:
- first_name: Julius von
full_name: Kügelgen, Julius von
last_name: Kügelgen
- first_name: Yash
full_name: Sharma, Yash
last_name: Sharma
- first_name: Luigi
full_name: Gresele, Luigi
last_name: Gresele
- first_name: Wieland
full_name: Brendel, Wieland
last_name: Brendel
- first_name: Bernhard
full_name: Schölkopf, Bernhard
last_name: Schölkopf
- first_name: Michel
full_name: Besserve, Michel
last_name: Besserve
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
citation:
ama: 'Kügelgen J von, Sharma Y, Gresele L, et al. Self-supervised learning with
data augmentations provably isolates content from style. In: Advances in Neural
Information Processing Systems. Vol 34. ; 2021:16451-16467.'
apa: Kügelgen, J. von, Sharma, Y., Gresele, L., Brendel, W., Schölkopf, B., Besserve,
M., & Locatello, F. (2021). Self-supervised learning with data augmentations
provably isolates content from style. In Advances in Neural Information Processing
Systems (Vol. 34, pp. 16451–16467). Virtual.
chicago: Kügelgen, Julius von, Yash Sharma, Luigi Gresele, Wieland Brendel, Bernhard
Schölkopf, Michel Besserve, and Francesco Locatello. “Self-Supervised Learning
with Data Augmentations Provably Isolates Content from Style.” In Advances
in Neural Information Processing Systems, 34:16451–67, 2021.
ieee: J. von Kügelgen et al., “Self-supervised learning with data augmentations
provably isolates content from style,” in Advances in Neural Information Processing
Systems, Virtual, 2021, vol. 34, pp. 16451–16467.
ista: 'Kügelgen J von, Sharma Y, Gresele L, Brendel W, Schölkopf B, Besserve M,
Locatello F. 2021. Self-supervised learning with data augmentations provably isolates
content from style. Advances in Neural Information Processing Systems. NeurIPS:
Neural Information Processing Systems vol. 34, 16451–16467.'
mla: Kügelgen, Julius von, et al. “Self-Supervised Learning with Data Augmentations
Provably Isolates Content from Style.” Advances in Neural Information Processing
Systems, vol. 34, 2021, pp. 16451–67.
short: J. von Kügelgen, Y. Sharma, L. Gresele, W. Brendel, B. Schölkopf, M. Besserve,
F. Locatello, in:, Advances in Neural Information Processing Systems, 2021, pp.
16451–16467.
conference:
end_date: 2021-12-10
location: Virtual
name: 'NeurIPS: Neural Information Processing Systems'
start_date: 2021-12-07
date_created: 2023-08-22T14:04:36Z
date_published: 2021-06-08T00:00:00Z
date_updated: 2023-09-11T10:33:19Z
day: '08'
department:
- _id: FrLo
extern: '1'
external_id:
arxiv:
- '2106.04619'
intvolume: ' 34'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2106.04619
month: '06'
oa: 1
oa_version: Preprint
page: 16451-16467
publication: Advances in Neural Information Processing Systems
publication_identifier:
isbn:
- '9781713845393'
publication_status: published
quality_controlled: '1'
status: public
title: Self-supervised learning with data augmentations provably isolates content
from style
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 34
year: '2021'
...
---
_id: '14180'
abstract:
- lang: eng
text: 'Modern neural network architectures can leverage large amounts of data to
generalize well within the training distribution. However, they are less capable
of systematic generalization to data drawn from unseen but related distributions,
a feat that is hypothesized to require compositional reasoning and reuse of knowledge.
In this work, we present Neural Interpreters, an architecture that factorizes
inference in a self-attention network as a system of modules, which we call \emph{functions}.
Inputs to the model are routed through a sequence of functions in a way that is
end-to-end learned. The proposed architecture can flexibly compose computation
along width and depth, and lends itself well to capacity extension after training.
To demonstrate the versatility of Neural Interpreters, we evaluate it in two distinct
settings: image classification and visual abstract reasoning on Raven Progressive
Matrices. In the former, we show that Neural Interpreters perform on par with
the vision transformer using fewer parameters, while being transferrable to a
new task in a sample efficient manner. In the latter, we find that Neural Interpreters
are competitive with respect to the state-of-the-art in terms of systematic generalization. '
article_processing_charge: No
arxiv: 1
author:
- first_name: Nasim
full_name: Rahaman, Nasim
last_name: Rahaman
- first_name: Muhammad Waleed
full_name: Gondal, Muhammad Waleed
last_name: Gondal
- first_name: Shruti
full_name: Joshi, Shruti
last_name: Joshi
- first_name: Peter
full_name: Gehler, Peter
last_name: Gehler
- first_name: Yoshua
full_name: Bengio, Yoshua
last_name: Bengio
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
- first_name: Bernhard
full_name: Schölkopf, Bernhard
last_name: Schölkopf
citation:
ama: 'Rahaman N, Gondal MW, Joshi S, et al. Dynamic inference with neural interpreters.
In: Advances in Neural Information Processing Systems. Vol 34. ; 2021:10985-10998.'
apa: Rahaman, N., Gondal, M. W., Joshi, S., Gehler, P., Bengio, Y., Locatello, F.,
& Schölkopf, B. (2021). Dynamic inference with neural interpreters. In Advances
in Neural Information Processing Systems (Vol. 34, pp. 10985–10998). Virtual.
chicago: Rahaman, Nasim, Muhammad Waleed Gondal, Shruti Joshi, Peter Gehler, Yoshua
Bengio, Francesco Locatello, and Bernhard Schölkopf. “Dynamic Inference with Neural
Interpreters.” In Advances in Neural Information Processing Systems, 34:10985–98,
2021.
ieee: N. Rahaman et al., “Dynamic inference with neural interpreters,” in
Advances in Neural Information Processing Systems, Virtual, 2021, vol.
34, pp. 10985–10998.
ista: 'Rahaman N, Gondal MW, Joshi S, Gehler P, Bengio Y, Locatello F, Schölkopf
B. 2021. Dynamic inference with neural interpreters. Advances in Neural Information
Processing Systems. NeurIPS: Neural Information Processing Systems vol. 34, 10985–10998.'
mla: Rahaman, Nasim, et al. “Dynamic Inference with Neural Interpreters.” Advances
in Neural Information Processing Systems, vol. 34, 2021, pp. 10985–98.
short: N. Rahaman, M.W. Gondal, S. Joshi, P. Gehler, Y. Bengio, F. Locatello, B.
Schölkopf, in:, Advances in Neural Information Processing Systems, 2021, pp. 10985–10998.
conference:
end_date: 2021-12-10
location: Virtual
name: 'NeurIPS: Neural Information Processing Systems'
start_date: 2021-12-07
date_created: 2023-08-22T14:04:55Z
date_published: 2021-10-12T00:00:00Z
date_updated: 2023-09-11T11:33:46Z
day: '12'
department:
- _id: FrLo
extern: '1'
external_id:
arxiv:
- '2110.06399'
intvolume: ' 34'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2110.06399
month: '10'
oa: 1
oa_version: Preprint
page: 10985-10998
publication: Advances in Neural Information Processing Systems
publication_identifier:
isbn:
- '9781713845393'
publication_status: published
quality_controlled: '1'
status: public
title: Dynamic inference with neural interpreters
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 34
year: '2021'
...
---
_id: '14181'
abstract:
- lang: eng
text: Variational Inference makes a trade-off between the capacity of the variational
family and the tractability of finding an approximate posterior distribution.
Instead, Boosting Variational Inference allows practitioners to obtain increasingly
good posterior approximations by spending more compute. The main obstacle to widespread
adoption of Boosting Variational Inference is the amount of resources necessary
to improve over a strong Variational Inference baseline. In our work, we trace
this limitation back to the global curvature of the KL-divergence. We characterize
how the global curvature impacts time and memory consumption, address the problem
with the notion of local curvature, and provide a novel approximate backtracking
algorithm for estimating local curvature. We give new theoretical convergence
rates for our algorithms and provide experimental validation on synthetic and
real-world datasets.
article_processing_charge: No
arxiv: 1
author:
- first_name: Gideon
full_name: Dresdner, Gideon
last_name: Dresdner
- first_name: Saurav
full_name: Shekhar, Saurav
last_name: Shekhar
- first_name: Fabian
full_name: Pedregosa, Fabian
last_name: Pedregosa
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
- first_name: Gunnar
full_name: Rätsch, Gunnar
last_name: Rätsch
citation:
ama: 'Dresdner G, Shekhar S, Pedregosa F, Locatello F, Rätsch G. Boosting variational
inference with locally adaptive step-sizes. In: Proceedings of the Thirtieth
International Joint Conference on Artificial Intelligence. International Joint
Conferences on Artificial Intelligence; 2021:2337-2343. doi:10.24963/ijcai.2021/322'
apa: 'Dresdner, G., Shekhar, S., Pedregosa, F., Locatello, F., & Rätsch, G.
(2021). Boosting variational inference with locally adaptive step-sizes. In Proceedings
of the Thirtieth International Joint Conference on Artificial Intelligence
(pp. 2337–2343). Montreal, Canada: International Joint Conferences on Artificial
Intelligence. https://doi.org/10.24963/ijcai.2021/322'
chicago: Dresdner, Gideon, Saurav Shekhar, Fabian Pedregosa, Francesco Locatello,
and Gunnar Rätsch. “Boosting Variational Inference with Locally Adaptive Step-Sizes.”
In Proceedings of the Thirtieth International Joint Conference on Artificial
Intelligence, 2337–43. International Joint Conferences on Artificial Intelligence,
2021. https://doi.org/10.24963/ijcai.2021/322.
ieee: G. Dresdner, S. Shekhar, F. Pedregosa, F. Locatello, and G. Rätsch, “Boosting
variational inference with locally adaptive step-sizes,” in Proceedings of
the Thirtieth International Joint Conference on Artificial Intelligence, Montreal,
Canada, 2021, pp. 2337–2343.
ista: 'Dresdner G, Shekhar S, Pedregosa F, Locatello F, Rätsch G. 2021. Boosting
variational inference with locally adaptive step-sizes. Proceedings of the Thirtieth
International Joint Conference on Artificial Intelligence. IJCAI: International
Joint Conference on Artificial Intelligence, 2337–2343.'
mla: Dresdner, Gideon, et al. “Boosting Variational Inference with Locally Adaptive
Step-Sizes.” Proceedings of the Thirtieth International Joint Conference on
Artificial Intelligence, International Joint Conferences on Artificial Intelligence,
2021, pp. 2337–43, doi:10.24963/ijcai.2021/322.
short: G. Dresdner, S. Shekhar, F. Pedregosa, F. Locatello, G. Rätsch, in:, Proceedings
of the Thirtieth International Joint Conference on Artificial Intelligence, International
Joint Conferences on Artificial Intelligence, 2021, pp. 2337–2343.
conference:
end_date: 2021-08-27
location: Montreal, Canada
name: 'IJCAI: International Joint Conference on Artificial Intelligence'
start_date: 2021-08-19
date_created: 2023-08-22T14:05:14Z
date_published: 2021-05-19T00:00:00Z
date_updated: 2023-09-11T11:14:30Z
day: '19'
department:
- _id: FrLo
doi: 10.24963/ijcai.2021/322
extern: '1'
external_id:
arxiv:
- '2105.09240'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2105.09240
month: '05'
oa: 1
oa_version: Published Version
page: 2337-2343
publication: Proceedings of the Thirtieth International Joint Conference on Artificial
Intelligence
publication_identifier:
eisbn:
- '9780999241196'
publication_status: published
publisher: International Joint Conferences on Artificial Intelligence
quality_controlled: '1'
status: public
title: Boosting variational inference with locally adaptive step-sizes
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2021'
...
---
_id: '14182'
abstract:
- lang: eng
text: "When machine learning systems meet real world applications, accuracy is only\r\none
of several requirements. In this paper, we assay a complementary\r\nperspective
originating from the increasing availability of pre-trained and\r\nregularly improving
state-of-the-art models. While new improved models develop\r\nat a fast pace,
downstream tasks vary more slowly or stay constant. Assume that\r\nwe have a large
unlabelled data set for which we want to maintain accurate\r\npredictions. Whenever
a new and presumably better ML models becomes available,\r\nwe encounter two problems:
(i) given a limited budget, which data points should\r\nbe re-evaluated using
the new model?; and (ii) if the new predictions differ\r\nfrom the current ones,
should we update? Problem (i) is about compute cost,\r\nwhich matters for very
large data sets and models. Problem (ii) is about\r\nmaintaining consistency of
the predictions, which can be highly relevant for\r\ndownstream applications;
our demand is to avoid negative flips, i.e., changing\r\ncorrect to incorrect
predictions. In this paper, we formalize the Prediction\r\nUpdate Problem and
present an efficient probabilistic approach as answer to the\r\nabove questions.
In extensive experiments on standard classification benchmark\r\ndata sets, we
show that our method outperforms alternative strategies along key\r\nmetrics for
backward-compatible prediction updates."
article_processing_charge: No
arxiv: 1
author:
- first_name: Frederik
full_name: Träuble, Frederik
last_name: Träuble
- first_name: Julius von
full_name: Kügelgen, Julius von
last_name: Kügelgen
- first_name: Matthäus
full_name: Kleindessner, Matthäus
last_name: Kleindessner
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
- first_name: Bernhard
full_name: Schölkopf, Bernhard
last_name: Schölkopf
- first_name: Peter
full_name: Gehler, Peter
last_name: Gehler
citation:
ama: 'Träuble F, Kügelgen J von, Kleindessner M, Locatello F, Schölkopf B, Gehler
P. Backward-compatible prediction updates: A probabilistic approach. In: 35th
Conference on Neural Information Processing Systems. Vol 34. ; 2021:116-128.'
apa: 'Träuble, F., Kügelgen, J. von, Kleindessner, M., Locatello, F., Schölkopf,
B., & Gehler, P. (2021). Backward-compatible prediction updates: A probabilistic
approach. In 35th Conference on Neural Information Processing Systems (Vol.
34, pp. 116–128). Virtual.'
chicago: 'Träuble, Frederik, Julius von Kügelgen, Matthäus Kleindessner, Francesco
Locatello, Bernhard Schölkopf, and Peter Gehler. “Backward-Compatible Prediction
Updates: A Probabilistic Approach.” In 35th Conference on Neural Information
Processing Systems, 34:116–28, 2021.'
ieee: 'F. Träuble, J. von Kügelgen, M. Kleindessner, F. Locatello, B. Schölkopf,
and P. Gehler, “Backward-compatible prediction updates: A probabilistic approach,”
in 35th Conference on Neural Information Processing Systems, Virtual, 2021,
vol. 34, pp. 116–128.'
ista: 'Träuble F, Kügelgen J von, Kleindessner M, Locatello F, Schölkopf B, Gehler
P. 2021. Backward-compatible prediction updates: A probabilistic approach. 35th
Conference on Neural Information Processing Systems. NeurIPS: Neural Information
Processing Systems vol. 34, 116–128.'
mla: 'Träuble, Frederik, et al. “Backward-Compatible Prediction Updates: A Probabilistic
Approach.” 35th Conference on Neural Information Processing Systems, vol.
34, 2021, pp. 116–28.'
short: F. Träuble, J. von Kügelgen, M. Kleindessner, F. Locatello, B. Schölkopf,
P. Gehler, in:, 35th Conference on Neural Information Processing Systems, 2021,
pp. 116–128.
conference:
end_date: 2021-12-10
location: Virtual
name: 'NeurIPS: Neural Information Processing Systems'
start_date: 2021-12-07
date_created: 2023-08-22T14:05:41Z
date_published: 2021-07-02T00:00:00Z
date_updated: 2023-09-11T11:31:59Z
day: '02'
department:
- _id: FrLo
extern: '1'
external_id:
arxiv:
- '2107.01057'
intvolume: ' 34'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2107.01057
month: '07'
oa: 1
oa_version: Preprint
page: 116-128
publication: 35th Conference on Neural Information Processing Systems
publication_identifier:
isbn:
- '9781713845393'
publication_status: published
quality_controlled: '1'
status: public
title: 'Backward-compatible prediction updates: A probabilistic approach'
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 34
year: '2021'
...
---
_id: '14221'
abstract:
- lang: eng
text: 'The world is structured in countless ways. It may be prudent to enforce corresponding
structural properties to a learning algorithm''s solution, such as incorporating
prior beliefs, natural constraints, or causal structures. Doing so may translate
to faster, more accurate, and more flexible models, which may directly relate
to real-world impact. In this dissertation, we consider two different research
areas that concern structuring a learning algorithm''s solution: when the structure
is known and when it has to be discovered.'
article_number: '2111.13693'
article_processing_charge: No
arxiv: 1
author:
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
citation:
ama: Locatello F. Enforcing and discovering structure in machine learning. arXiv.
doi:10.48550/arXiv.2111.13693
apa: Locatello, F. (n.d.). Enforcing and discovering structure in machine learning.
arXiv. https://doi.org/10.48550/arXiv.2111.13693
chicago: Locatello, Francesco. “Enforcing and Discovering Structure in Machine Learning.”
ArXiv, n.d. https://doi.org/10.48550/arXiv.2111.13693.
ieee: F. Locatello, “Enforcing and discovering structure in machine learning,” arXiv.
.
ista: Locatello F. Enforcing and discovering structure in machine learning. arXiv,
2111.13693.
mla: Locatello, Francesco. “Enforcing and Discovering Structure in Machine Learning.”
ArXiv, 2111.13693, doi:10.48550/arXiv.2111.13693.
short: F. Locatello, ArXiv (n.d.).
date_created: 2023-08-22T14:23:35Z
date_published: 2021-11-26T00:00:00Z
date_updated: 2023-09-12T07:04:44Z
day: '26'
department:
- _id: FrLo
doi: 10.48550/arXiv.2111.13693
extern: '1'
external_id:
arxiv:
- '2111.13693'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2111.13693
month: '11'
oa: 1
oa_version: Preprint
publication: arXiv
publication_status: submitted
status: public
title: Enforcing and discovering structure in machine learning
type: preprint
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2021'
...
---
_id: '14278'
abstract:
- lang: eng
text: 'The Birkhoff conjecture says that the boundary of a strictly convex integrable
billiard table is necessarily an ellipse. In this article, we consider a stronger
notion of integrability, namely, integrability close to the boundary, and prove
a local version of this conjecture: a small perturbation of almost every ellipse
that preserves integrability near the boundary, is itself an ellipse. We apply
this result to study local spectral rigidity of ellipses using the connection
between the wave trace of the Laplacian and the dynamics near the boundary and
establish rigidity for almost all of them.'
article_number: '2111.12171'
article_processing_charge: No
arxiv: 1
author:
- first_name: Illya
full_name: Koval, Illya
id: 2eed1f3b-896a-11ed-bdf8-93c7c4bf159e
last_name: Koval
citation:
ama: Koval I. Local strong Birkhoff conjecture and local spectral rigidity of almost
every ellipse. arXiv. doi:10.48550/ARXIV.2111.12171
apa: Koval, I. (n.d.). Local strong Birkhoff conjecture and local spectral rigidity
of almost every ellipse. arXiv. https://doi.org/10.48550/ARXIV.2111.12171
chicago: Koval, Illya. “Local Strong Birkhoff Conjecture and Local Spectral Rigidity
of Almost Every Ellipse.” ArXiv, n.d. https://doi.org/10.48550/ARXIV.2111.12171.
ieee: I. Koval, “Local strong Birkhoff conjecture and local spectral rigidity of
almost every ellipse,” arXiv. .
ista: Koval I. Local strong Birkhoff conjecture and local spectral rigidity of almost
every ellipse. arXiv, 2111.12171.
mla: Koval, Illya. “Local Strong Birkhoff Conjecture and Local Spectral Rigidity
of Almost Every Ellipse.” ArXiv, 2111.12171, doi:10.48550/ARXIV.2111.12171.
short: I. Koval, ArXiv (n.d.).
date_created: 2023-09-06T08:35:43Z
date_published: 2021-11-23T00:00:00Z
date_updated: 2023-09-15T06:44:00Z
day: '23'
department:
- _id: GradSch
doi: 10.48550/ARXIV.2111.12171
external_id:
arxiv:
- '2111.12171'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2111.12171
month: '11'
oa: 1
oa_version: Preprint
publication: arXiv
publication_status: submitted
status: public
title: Local strong Birkhoff conjecture and local spectral rigidity of almost every
ellipse
type: preprint
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2021'
...
---
_id: '14332'
abstract:
- lang: eng
text: Learning data representations that are useful for various downstream tasks
is a cornerstone of artificial intelligence. While existing methods are typically
evaluated on downstream tasks such as classification or generative image quality,
we propose to assess representations through their usefulness in downstream control
tasks, such as reaching or pushing objects. By training over 10,000 reinforcement
learning policies, we extensively evaluate to what extent different representation
properties affect out-of-distribution (OOD) generalization. Finally, we demonstrate
zero-shot transfer of these policies from simulation to the real world, without
any domain randomization or fine-tuning. This paper aims to establish the first
systematic characterization of the usefulness of learned representations for real-world
OOD downstream tasks.
article_processing_charge: No
author:
- first_name: Frederik
full_name: Träuble, Frederik
last_name: Träuble
- first_name: Andrea
full_name: Dittadi, Andrea
last_name: Dittadi
- first_name: Manuel
full_name: Wuthrich, Manuel
last_name: Wuthrich
- first_name: Felix
full_name: Widmaier, Felix
last_name: Widmaier
- first_name: Peter Vincent
full_name: Gehler, Peter Vincent
last_name: Gehler
- first_name: Ole
full_name: Winther, Ole
last_name: Winther
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
- first_name: Olivier
full_name: Bachem, Olivier
last_name: Bachem
- first_name: Bernhard
full_name: Schölkopf, Bernhard
last_name: Schölkopf
- first_name: Stefan
full_name: Bauer, Stefan
last_name: Bauer
citation:
ama: 'Träuble F, Dittadi A, Wuthrich M, et al. Representation learning for out-of-distribution
generalization in reinforcement learning. In: ICML 2021 Workshop on Unsupervised
Reinforcement Learning. ; 2021.'
apa: Träuble, F., Dittadi, A., Wuthrich, M., Widmaier, F., Gehler, P. V., Winther,
O., … Bauer, S. (2021). Representation learning for out-of-distribution generalization
in reinforcement learning. In ICML 2021 Workshop on Unsupervised Reinforcement
Learning. Virtual.
chicago: Träuble, Frederik, Andrea Dittadi, Manuel Wuthrich, Felix Widmaier, Peter
Vincent Gehler, Ole Winther, Francesco Locatello, Olivier Bachem, Bernhard Schölkopf,
and Stefan Bauer. “Representation Learning for Out-of-Distribution Generalization
in Reinforcement Learning.” In ICML 2021 Workshop on Unsupervised Reinforcement
Learning, 2021.
ieee: F. Träuble et al., “Representation learning for out-of-distribution
generalization in reinforcement learning,” in ICML 2021 Workshop on Unsupervised
Reinforcement Learning, Virtual, 2021.
ista: 'Träuble F, Dittadi A, Wuthrich M, Widmaier F, Gehler PV, Winther O, Locatello
F, Bachem O, Schölkopf B, Bauer S. 2021. Representation learning for out-of-distribution
generalization in reinforcement learning. ICML 2021 Workshop on Unsupervised Reinforcement
Learning. ICML: International Conference on Machine Learning.'
mla: Träuble, Frederik, et al. “Representation Learning for Out-of-Distribution
Generalization in Reinforcement Learning.” ICML 2021 Workshop on Unsupervised
Reinforcement Learning, 2021.
short: F. Träuble, A. Dittadi, M. Wuthrich, F. Widmaier, P.V. Gehler, O. Winther,
F. Locatello, O. Bachem, B. Schölkopf, S. Bauer, in:, ICML 2021 Workshop on Unsupervised
Reinforcement Learning, 2021.
conference:
end_date: 2021-07-23
location: Virtual
name: 'ICML: International Conference on Machine Learning'
start_date: 2021-07-23
date_created: 2023-09-13T12:43:14Z
date_published: 2021-07-23T00:00:00Z
date_updated: 2023-09-13T12:44:00Z
day: '23'
department:
- _id: FrLo
extern: '1'
language:
- iso: eng
month: '07'
oa_version: None
publication: ICML 2021 Workshop on Unsupervised Reinforcement Learning
publication_status: published
quality_controlled: '1'
status: public
title: Representation learning for out-of-distribution generalization in reinforcement
learning
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2021'
...
---
_id: '10000'
abstract:
- lang: eng
text: Inhibition or targeted deletion of histone deacetylase 3 (HDAC3) is neuroprotective
in a variety neurodegenerative conditions, including retinal ganglion cells (RGCs)
after acute optic nerve damage. Consistent with this, induced HDAC3 expression
in cultured cells shows selective toxicity to neurons. Despite an established
role for HDAC3 in neuronal pathology, little is known regarding the mechanism
of this pathology.
acknowledgement: 'The authors thank Joel Dietz for maintaining the mice used in this
study, Satoshi Kinoshita and the Translational Research Initiative in Pathology
Laboratory at the University of Wisconsin-Madison for cutting retinal sections analyzed
in this study, and Mark Banghart for statistical review of the data analysis. Supported
by National Eye Institute Grants R01 EY012223 (RWN), R01 EY030123 (RWN), R01 EY029809
(LWG), R01 EY029809 (LWG) and a Vision Research CORE grant P30 EY016665, NRSA grant
T32 GM081061, by an unrestricted research grant from Research to Prevent Blindness,
Inc., and by a University of Wisconsin-Madison Vilas Life Cycle award and the Frederick
A. Davis Research Chair (RWN). '
article_number: '14'
article_processing_charge: Yes
article_type: original
author:
- first_name: Heather M.
full_name: Schmitt, Heather M.
last_name: Schmitt
- first_name: Rachel L.
full_name: Fehrman, Rachel L.
last_name: Fehrman
- first_name: Margaret E
full_name: Maes, Margaret E
id: 3838F452-F248-11E8-B48F-1D18A9856A87
last_name: Maes
orcid: 0000-0001-9642-1085
- first_name: Huan
full_name: Yang, Huan
last_name: Yang
- first_name: Lian Wang
full_name: Guo, Lian Wang
last_name: Guo
- first_name: Cassandra L.
full_name: Schlamp, Cassandra L.
last_name: Schlamp
- first_name: Heather R.
full_name: Pelzel, Heather R.
last_name: Pelzel
- first_name: Robert W.
full_name: Nickells, Robert W.
last_name: Nickells
citation:
ama: Schmitt HM, Fehrman RL, Maes ME, et al. Increased susceptibility and intrinsic
apoptotic signaling in neurons by induced HDAC3 expression. Investigative Ophthalmology
and Visual Science. 2021;62(10). doi:10.1167/IOVS.62.10.14
apa: Schmitt, H. M., Fehrman, R. L., Maes, M. E., Yang, H., Guo, L. W., Schlamp,
C. L., … Nickells, R. W. (2021). Increased susceptibility and intrinsic apoptotic
signaling in neurons by induced HDAC3 expression. Investigative Ophthalmology
and Visual Science. Association for Research in Vision and Ophthalmology.
https://doi.org/10.1167/IOVS.62.10.14
chicago: Schmitt, Heather M., Rachel L. Fehrman, Margaret E Maes, Huan Yang, Lian
Wang Guo, Cassandra L. Schlamp, Heather R. Pelzel, and Robert W. Nickells. “Increased
Susceptibility and Intrinsic Apoptotic Signaling in Neurons by Induced HDAC3 Expression.”
Investigative Ophthalmology and Visual Science. Association for Research
in Vision and Ophthalmology, 2021. https://doi.org/10.1167/IOVS.62.10.14.
ieee: H. M. Schmitt et al., “Increased susceptibility and intrinsic apoptotic
signaling in neurons by induced HDAC3 expression,” Investigative Ophthalmology
and Visual Science, vol. 62, no. 10. Association for Research in Vision and
Ophthalmology, 2021.
ista: Schmitt HM, Fehrman RL, Maes ME, Yang H, Guo LW, Schlamp CL, Pelzel HR, Nickells
RW. 2021. Increased susceptibility and intrinsic apoptotic signaling in neurons
by induced HDAC3 expression. Investigative Ophthalmology and Visual Science. 62(10),
14.
mla: Schmitt, Heather M., et al. “Increased Susceptibility and Intrinsic Apoptotic
Signaling in Neurons by Induced HDAC3 Expression.” Investigative Ophthalmology
and Visual Science, vol. 62, no. 10, 14, Association for Research in Vision
and Ophthalmology, 2021, doi:10.1167/IOVS.62.10.14.
short: H.M. Schmitt, R.L. Fehrman, M.E. Maes, H. Yang, L.W. Guo, C.L. Schlamp, H.R.
Pelzel, R.W. Nickells, Investigative Ophthalmology and Visual Science 62 (2021).
date_created: 2021-09-12T22:01:23Z
date_published: 2021-08-16T00:00:00Z
date_updated: 2023-08-14T06:35:17Z
day: '16'
ddc:
- '570'
department:
- _id: SaSi
doi: 10.1167/IOVS.62.10.14
external_id:
isi:
- '000695230000014'
pmid:
- '34398198'
file:
- access_level: open_access
checksum: c430967746f653aa1ae84ee617f62b73
content_type: application/pdf
creator: dernst
date_created: 2022-05-13T07:40:15Z
date_updated: 2022-05-13T07:40:15Z
file_id: '11369'
file_name: 2021_IOVS_Schmitt.pdf
file_size: 19707796
relation: main_file
success: 1
file_date_updated: 2022-05-13T07:40:15Z
has_accepted_license: '1'
intvolume: ' 62'
isi: 1
issue: '10'
language:
- iso: eng
month: '08'
oa: 1
oa_version: Published Version
pmid: 1
publication: Investigative Ophthalmology and Visual Science
publication_identifier:
eissn:
- 1552-5783
issn:
- 0146-0404
publication_status: published
publisher: Association for Research in Vision and Ophthalmology
quality_controlled: '1'
scopus_import: '1'
status: public
title: Increased susceptibility and intrinsic apoptotic signaling in neurons by induced
HDAC3 expression
tmp:
image: /images/cc_by_nc_nd.png
legal_code_url: https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode
name: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
(CC BY-NC-ND 4.0)
short: CC BY-NC-ND (4.0)
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 62
year: '2021'
...
---
_id: '10002'
abstract:
- lang: eng
text: 'We present a faster symbolic algorithm for the following central problem
in probabilistic verification: Compute the maximal end-component (MEC) decomposition
of Markov decision processes (MDPs). This problem generalizes the SCC decomposition
problem of graphs and closed recurrent sets of Markov chains. The model of symbolic
algorithms is widely used in formal verification and model-checking, where access
to the input model is restricted to only symbolic operations (e.g., basic set
operations and computation of one-step neighborhood). For an input MDP with n vertices
and m edges, the classical symbolic algorithm from the 1990s for the MEC decomposition
requires O(n2) symbolic operations and O(1) symbolic space. The only other
symbolic algorithm for the MEC decomposition requires O(nm−−√) symbolic operations
and O(m−−√) symbolic space. A main open question is whether the worst-case O(n2) bound
for symbolic operations can be beaten. We present a symbolic algorithm that requires O˜(n1.5) symbolic
operations and O˜(n−−√) symbolic space. Moreover, the parametrization of our
algorithm provides a trade-off between symbolic operations and symbolic space:
for all 0<ϵ≤1/2 the symbolic algorithm requires O˜(n2−ϵ) symbolic operations
and O˜(nϵ) symbolic space ( O˜ hides poly-logarithmic factors). Using our techniques
we present faster algorithms for computing the almost-sure winning regions of ω
-regular objectives for MDPs. We consider the canonical parity objectives for ω
-regular objectives, and for parity objectives with d -priorities we present
an algorithm that computes the almost-sure winning region with O˜(n2−ϵ) symbolic
operations and O˜(nϵ) symbolic space, for all 0<ϵ≤1/2 .'
acknowledgement: The authors are grateful to the anonymous referees for their valuable
comments. A. S. is fully supported by the Vienna Science and Technology Fund (WWTF)
through project ICT15–003. K. C. is supported by the Austrian Science Fund (FWF)
NFN Grant No S11407-N23 (RiSE/SHiNE) and by the ERC CoG 863818 (ForM-SMArt). For
M. H. the research leading to these results has received funding from the European
Research Council under the European Unions Seventh Framework Programme (FP/2007–2013)
/ ERC Grant Agreement no. 340506.
article_processing_charge: No
arxiv: 1
author:
- first_name: Krishnendu
full_name: Chatterjee, Krishnendu
id: 2E5DCA20-F248-11E8-B48F-1D18A9856A87
last_name: Chatterjee
orcid: 0000-0002-4561-241X
- first_name: Wolfgang
full_name: Dvorak, Wolfgang
last_name: Dvorak
- first_name: Monika H
full_name: Henzinger, Monika H
id: 540c9bbd-f2de-11ec-812d-d04a5be85630
last_name: Henzinger
orcid: 0000-0002-5008-6530
- first_name: Alexander
full_name: Svozil, Alexander
last_name: Svozil
citation:
ama: 'Chatterjee K, Dvorak W, Henzinger MH, Svozil A. Symbolic time and space tradeoffs
for probabilistic verification. In: Proceedings of the 36th Annual ACM/IEEE
Symposium on Logic in Computer Science. Institute of Electrical and Electronics
Engineers; 2021:1-13. doi:10.1109/LICS52264.2021.9470739'
apa: 'Chatterjee, K., Dvorak, W., Henzinger, M. H., & Svozil, A. (2021). Symbolic
time and space tradeoffs for probabilistic verification. In Proceedings of
the 36th Annual ACM/IEEE Symposium on Logic in Computer Science (pp. 1–13).
Rome, Italy: Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/LICS52264.2021.9470739'
chicago: Chatterjee, Krishnendu, Wolfgang Dvorak, Monika H Henzinger, and Alexander
Svozil. “Symbolic Time and Space Tradeoffs for Probabilistic Verification.” In
Proceedings of the 36th Annual ACM/IEEE Symposium on Logic in Computer Science,
1–13. Institute of Electrical and Electronics Engineers, 2021. https://doi.org/10.1109/LICS52264.2021.9470739.
ieee: K. Chatterjee, W. Dvorak, M. H. Henzinger, and A. Svozil, “Symbolic time and
space tradeoffs for probabilistic verification,” in Proceedings of the 36th
Annual ACM/IEEE Symposium on Logic in Computer Science, Rome, Italy, 2021,
pp. 1–13.
ista: 'Chatterjee K, Dvorak W, Henzinger MH, Svozil A. 2021. Symbolic time and space
tradeoffs for probabilistic verification. Proceedings of the 36th Annual ACM/IEEE
Symposium on Logic in Computer Science. LICS: Symposium on Logic in Computer Science,
1–13.'
mla: Chatterjee, Krishnendu, et al. “Symbolic Time and Space Tradeoffs for Probabilistic
Verification.” Proceedings of the 36th Annual ACM/IEEE Symposium on Logic in
Computer Science, Institute of Electrical and Electronics Engineers, 2021,
pp. 1–13, doi:10.1109/LICS52264.2021.9470739.
short: K. Chatterjee, W. Dvorak, M.H. Henzinger, A. Svozil, in:, Proceedings of
the 36th Annual ACM/IEEE Symposium on Logic in Computer Science, Institute of
Electrical and Electronics Engineers, 2021, pp. 1–13.
conference:
end_date: 2021-07-02
location: Rome, Italy
name: 'LICS: Symposium on Logic in Computer Science'
start_date: 2021-06-29
date_created: 2021-09-12T22:01:24Z
date_published: 2021-07-07T00:00:00Z
date_updated: 2025-07-14T09:10:07Z
day: '07'
department:
- _id: KrCh
doi: 10.1109/LICS52264.2021.9470739
ec_funded: 1
external_id:
arxiv:
- '2104.07466'
isi:
- '000947350400089'
isi: 1
keyword:
- Computer science
- Computational modeling
- Markov processes
- Probabilistic logic
- Formal verification
- Game Theory
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2104.07466
month: '07'
oa: 1
oa_version: Preprint
page: 1-13
project:
- _id: 25863FF4-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: S11407
name: Game Theory
- _id: 0599E47C-7A3F-11EA-A408-12923DDC885E
call_identifier: H2020
grant_number: '863818'
name: 'Formal Methods for Stochastic Models: Algorithms and Applications'
publication: Proceedings of the 36th Annual ACM/IEEE Symposium on Logic in Computer
Science
publication_identifier:
eisbn:
- 978-1-6654-4895-6
isbn:
- 978-1-6654-4896-3
issn:
- 1043-6871
publication_status: published
publisher: Institute of Electrical and Electronics Engineers
quality_controlled: '1'
scopus_import: '1'
status: public
title: Symbolic time and space tradeoffs for probabilistic verification
type: conference
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
year: '2021'
...
---
_id: '10004'
abstract:
- lang: eng
text: 'Markov chains are the de facto finite-state model for stochastic dynamical
systems, and Markov decision processes (MDPs) extend Markov chains by incorporating
non-deterministic behaviors. Given an MDP and rewards on states, a classical optimization
criterion is the maximal expected total reward where the MDP stops after T steps,
which can be computed by a simple dynamic programming algorithm. We consider a
natural generalization of the problem where the stopping times can be chosen according
to a probability distribution, such that the expected stopping time is T, to optimize
the expected total reward. Quite surprisingly we establish inter-reducibility
of the expected stopping-time problem for Markov chains with the Positivity problem
(which is related to the well-known Skolem problem), for which establishing either
decidability or undecidability would be a major breakthrough. Given the hardness
of the exact problem, we consider the approximate version of the problem: we show
that it can be solved in exponential time for Markov chains and in exponential
space for MDPs.'
acknowledgement: We are grateful to the anonymous reviewers of LICS 2021 and of a
previous version of this paper for insightful comments that helped improving the
presentation. This research was partially supported by the grant ERC CoG 863818
(ForM-SMArt).
article_processing_charge: No
arxiv: 1
author:
- first_name: Krishnendu
full_name: Chatterjee, Krishnendu
id: 2E5DCA20-F248-11E8-B48F-1D18A9856A87
last_name: Chatterjee
orcid: 0000-0002-4561-241X
- first_name: Laurent
full_name: Doyen, Laurent
last_name: Doyen
citation:
ama: 'Chatterjee K, Doyen L. Stochastic processes with expected stopping time. In:
Proceedings of the 36th Annual ACM/IEEE Symposium on Logic in Computer Science.
Institute of Electrical and Electronics Engineers; 2021:1-13. doi:10.1109/LICS52264.2021.9470595'
apa: 'Chatterjee, K., & Doyen, L. (2021). Stochastic processes with expected
stopping time. In Proceedings of the 36th Annual ACM/IEEE Symposium on Logic
in Computer Science (pp. 1–13). Rome, Italy: Institute of Electrical and Electronics
Engineers. https://doi.org/10.1109/LICS52264.2021.9470595'
chicago: Chatterjee, Krishnendu, and Laurent Doyen. “Stochastic Processes with Expected
Stopping Time.” In Proceedings of the 36th Annual ACM/IEEE Symposium on Logic
in Computer Science, 1–13. Institute of Electrical and Electronics Engineers,
2021. https://doi.org/10.1109/LICS52264.2021.9470595.
ieee: K. Chatterjee and L. Doyen, “Stochastic processes with expected stopping time,”
in Proceedings of the 36th Annual ACM/IEEE Symposium on Logic in Computer Science,
Rome, Italy, 2021, pp. 1–13.
ista: 'Chatterjee K, Doyen L. 2021. Stochastic processes with expected stopping
time. Proceedings of the 36th Annual ACM/IEEE Symposium on Logic in Computer Science.
LICS: Symposium on Logic in Computer Science, 1–13.'
mla: Chatterjee, Krishnendu, and Laurent Doyen. “Stochastic Processes with Expected
Stopping Time.” Proceedings of the 36th Annual ACM/IEEE Symposium on Logic
in Computer Science, Institute of Electrical and Electronics Engineers, 2021,
pp. 1–13, doi:10.1109/LICS52264.2021.9470595.
short: K. Chatterjee, L. Doyen, in:, Proceedings of the 36th Annual ACM/IEEE Symposium
on Logic in Computer Science, Institute of Electrical and Electronics Engineers,
2021, pp. 1–13.
conference:
end_date: 2021-07-02
location: Rome, Italy
name: 'LICS: Symposium on Logic in Computer Science'
start_date: 2021-06-29
date_created: 2021-09-12T22:01:25Z
date_published: 2021-07-07T00:00:00Z
date_updated: 2025-07-14T09:10:08Z
day: '07'
department:
- _id: KrCh
doi: 10.1109/LICS52264.2021.9470595
ec_funded: 1
external_id:
arxiv:
- '2104.07278'
isi:
- '000947350400036'
isi: 1
keyword:
- Computer science
- Heuristic algorithms
- Memory management
- Automata
- Markov processes
- Probability distribution
- Complexity theory
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2104.07278
month: '07'
oa: 1
oa_version: Preprint
page: 1-13
project:
- _id: 0599E47C-7A3F-11EA-A408-12923DDC885E
call_identifier: H2020
grant_number: '863818'
name: 'Formal Methods for Stochastic Models: Algorithms and Applications'
publication: Proceedings of the 36th Annual ACM/IEEE Symposium on Logic in Computer
Science
publication_identifier:
eisbn:
- 978-1-6654-4895-6
isbn:
- 978-1-6654-4896-3
issn:
- 1043-6871
publication_status: published
publisher: Institute of Electrical and Electronics Engineers
quality_controlled: '1'
scopus_import: '1'
status: public
title: Stochastic processes with expected stopping time
type: conference
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
year: '2021'
...
---
_id: '10005'
abstract:
- lang: eng
text: We study systems of nonlinear partial differential equations of parabolic
type, in which the elliptic operator is replaced by the first-order divergence
operator acting on a flux function, which is related to the spatial gradient of
the unknown through an additional implicit equation. This setting, broad enough
in terms of applications, significantly expands the paradigm of nonlinear parabolic
problems. Formulating four conditions concerning the form of the implicit equation,
we first show that these conditions describe a maximal monotone p-coercive graph.
We then establish the global-in-time and large-data existence of a (weak) solution
and its uniqueness. To this end, we adopt and significantly generalize Minty’s
method of monotone mappings. A unified theory, containing several novel tools,
is developed in a way to be tractable from the point of view of numerical approximations.
acknowledgement: "M. Bulíček and J. Málek acknowledge the support of the project No.
18-12719S financed by the Czech\r\nScience foundation (GAČR). E. Maringová acknowledges
support from Charles University Research program \r\nUNCE/SCI/023, the grant SVV-2020-260583
by the Ministry of Education, Youth and Sports, Czech Republic\r\nand from the Austrian
Science Fund (FWF), grants P30000, W1245, and F65. M. Bulíček and J. Málek are\r\nmembers
of the Nečas Center for Mathematical Modelling.\r\n"
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Miroslav
full_name: Bulíček, Miroslav
last_name: Bulíček
- first_name: Erika
full_name: Maringová, Erika
id: dbabca31-66eb-11eb-963a-fb9c22c880b4
last_name: Maringová
- first_name: Josef
full_name: Málek, Josef
last_name: Málek
citation:
ama: Bulíček M, Maringová E, Málek J. On nonlinear problems of parabolic type with
implicit constitutive equations involving flux. Mathematical Models and Methods
in Applied Sciences. 2021;31(09). doi:10.1142/S0218202521500457
apa: Bulíček, M., Maringová, E., & Málek, J. (2021). On nonlinear problems of
parabolic type with implicit constitutive equations involving flux. Mathematical
Models and Methods in Applied Sciences. World Scientific. https://doi.org/10.1142/S0218202521500457
chicago: Bulíček, Miroslav, Erika Maringová, and Josef Málek. “On Nonlinear Problems
of Parabolic Type with Implicit Constitutive Equations Involving Flux.” Mathematical
Models and Methods in Applied Sciences. World Scientific, 2021. https://doi.org/10.1142/S0218202521500457.
ieee: M. Bulíček, E. Maringová, and J. Málek, “On nonlinear problems of parabolic
type with implicit constitutive equations involving flux,” Mathematical Models
and Methods in Applied Sciences, vol. 31, no. 09. World Scientific, 2021.
ista: Bulíček M, Maringová E, Málek J. 2021. On nonlinear problems of parabolic
type with implicit constitutive equations involving flux. Mathematical Models
and Methods in Applied Sciences. 31(09).
mla: Bulíček, Miroslav, et al. “On Nonlinear Problems of Parabolic Type with Implicit
Constitutive Equations Involving Flux.” Mathematical Models and Methods in
Applied Sciences, vol. 31, no. 09, World Scientific, 2021, doi:10.1142/S0218202521500457.
short: M. Bulíček, E. Maringová, J. Málek, Mathematical Models and Methods in Applied
Sciences 31 (2021).
date_created: 2021-09-12T22:01:25Z
date_published: 2021-08-25T00:00:00Z
date_updated: 2023-09-04T11:43:45Z
day: '25'
department:
- _id: JuFi
doi: 10.1142/S0218202521500457
external_id:
arxiv:
- '2009.06917'
isi:
- '000722222900004'
intvolume: ' 31'
isi: 1
issue: '09'
keyword:
- Nonlinear parabolic systems
- implicit constitutive theory
- weak solutions
- existence
- uniqueness
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2009.06917
month: '08'
oa: 1
oa_version: Preprint
project:
- _id: fc31cba2-9c52-11eb-aca3-ff467d239cd2
grant_number: F6504
name: Taming Complexity in Partial Differential Systems
publication: Mathematical Models and Methods in Applied Sciences
publication_identifier:
eissn:
- 1793-6314
issn:
- 0218-2025
publication_status: published
publisher: World Scientific
quality_controlled: '1'
scopus_import: '1'
status: public
title: On nonlinear problems of parabolic type with implicit constitutive equations
involving flux
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 31
year: '2021'
...
---
_id: '10007'
abstract:
- lang: eng
text: The present thesis is concerned with the derivation of weak-strong uniqueness
principles for curvature driven interface evolution problems not satisfying a
comparison principle. The specific examples being treated are two-phase Navier-Stokes
flow with surface tension, modeling the evolution of two incompressible, viscous
and immiscible fluids separated by a sharp interface, and multiphase mean curvature
flow, which serves as an idealized model for the motion of grain boundaries in
an annealing polycrystalline material. Our main results - obtained in joint works
with Julian Fischer, Tim Laux and Theresa M. Simon - state that prior to the formation
of geometric singularities due to topology changes, the weak solution concept
of Abels (Interfaces Free Bound. 9, 2007) to two-phase Navier-Stokes flow with
surface tension and the weak solution concept of Laux and Otto (Calc. Var. Partial
Differential Equations 55, 2016) to multiphase mean curvature flow (for networks
in R^2 or double bubbles in R^3) represents the unique solution to these interface
evolution problems within the class of classical solutions, respectively. To the
best of the author's knowledge, for interface evolution problems not admitting
a geometric comparison principle the derivation of a weak-strong uniqueness principle
represented an open problem, so that the works contained in the present thesis
constitute the first positive results in this direction. The key ingredient of
our approach consists of the introduction of a novel concept of relative entropies
for a class of curvature driven interface evolution problems, for which the associated
energy contains an interfacial contribution being proportional to the surface
area of the evolving (network of) interface(s). The interfacial part of the relative
entropy gives sufficient control on the interface error between a weak and a classical
solution, and its time evolution can be computed, at least in principle, for any
energy dissipating weak solution concept. A resulting stability estimate for the
relative entropy essentially entails the above mentioned weak-strong uniqueness
principles. The present thesis contains a detailed introduction to our relative
entropy approach, which in particular highlights potential applications to other
problems in curvature driven interface evolution not treated in this thesis.
alternative_title:
- ISTA Thesis
article_processing_charge: No
author:
- first_name: Sebastian
full_name: Hensel, Sebastian
id: 4D23B7DA-F248-11E8-B48F-1D18A9856A87
last_name: Hensel
orcid: 0000-0001-7252-8072
citation:
ama: 'Hensel S. Curvature driven interface evolution: Uniqueness properties of weak
solution concepts. 2021. doi:10.15479/at:ista:10007'
apa: 'Hensel, S. (2021). Curvature driven interface evolution: Uniqueness properties
of weak solution concepts. Institute of Science and Technology Austria. https://doi.org/10.15479/at:ista:10007'
chicago: 'Hensel, Sebastian. “Curvature Driven Interface Evolution: Uniqueness Properties
of Weak Solution Concepts.” Institute of Science and Technology Austria, 2021.
https://doi.org/10.15479/at:ista:10007.'
ieee: 'S. Hensel, “Curvature driven interface evolution: Uniqueness properties of
weak solution concepts,” Institute of Science and Technology Austria, 2021.'
ista: 'Hensel S. 2021. Curvature driven interface evolution: Uniqueness properties
of weak solution concepts. Institute of Science and Technology Austria.'
mla: 'Hensel, Sebastian. Curvature Driven Interface Evolution: Uniqueness Properties
of Weak Solution Concepts. Institute of Science and Technology Austria, 2021,
doi:10.15479/at:ista:10007.'
short: 'S. Hensel, Curvature Driven Interface Evolution: Uniqueness Properties of
Weak Solution Concepts, Institute of Science and Technology Austria, 2021.'
date_created: 2021-09-13T11:12:34Z
date_published: 2021-09-14T00:00:00Z
date_updated: 2023-09-07T13:30:45Z
day: '14'
ddc:
- '515'
degree_awarded: PhD
department:
- _id: GradSch
- _id: JuFi
doi: 10.15479/at:ista:10007
ec_funded: 1
file:
- access_level: closed
checksum: c8475faaf0b680b4971f638f1db16347
content_type: application/x-zip-compressed
creator: shensel
date_created: 2021-09-13T11:03:24Z
date_updated: 2021-09-15T14:37:30Z
file_id: '10008'
file_name: thesis_final_Hensel.zip
file_size: 15022154
relation: source_file
- access_level: open_access
checksum: 1a609937aa5275452822f45f2da17f07
content_type: application/pdf
creator: shensel
date_created: 2021-09-13T14:18:56Z
date_updated: 2021-09-14T09:52:47Z
file_id: '10014'
file_name: thesis_final_Hensel.pdf
file_size: 6583638
relation: main_file
file_date_updated: 2021-09-15T14:37:30Z
has_accepted_license: '1'
language:
- iso: eng
month: '09'
oa: 1
oa_version: Published Version
page: '300'
project:
- _id: 2564DBCA-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '665385'
name: International IST Doctoral Program
- _id: 0aa76401-070f-11eb-9043-b5bb049fa26d
call_identifier: H2020
grant_number: '948819'
name: Bridging Scales in Random Materials
publication_identifier:
issn:
- 2663-337X
publication_status: published
publisher: Institute of Science and Technology Austria
related_material:
record:
- id: '10012'
relation: part_of_dissertation
status: public
- id: '10013'
relation: part_of_dissertation
status: public
- id: '7489'
relation: part_of_dissertation
status: public
status: public
supervisor:
- first_name: Julian L
full_name: Fischer, Julian L
id: 2C12A0B0-F248-11E8-B48F-1D18A9856A87
last_name: Fischer
orcid: 0000-0002-0479-558X
title: 'Curvature driven interface evolution: Uniqueness properties of weak solution
concepts'
type: dissertation
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
year: '2021'
...
---
_id: '10011'
abstract:
- lang: eng
text: We propose a new weak solution concept for (two-phase) mean curvature flow
which enjoys both (unconditional) existence and (weak-strong) uniqueness properties.
These solutions are evolving varifolds, just as in Brakke's formulation, but are
coupled to the phase volumes by a simple transport equation. First, we show that,
in the exact same setup as in Ilmanen's proof [J. Differential Geom. 38, 417-461,
(1993)], any limit point of solutions to the Allen-Cahn equation is a varifold
solution in our sense. Second, we prove that any calibrated flow in the sense
of Fischer et al. [arXiv:2003.05478] - and hence any classical solution to mean
curvature flow - is unique in the class of our new varifold solutions. This is
in sharp contrast to the case of Brakke flows, which a priori may disappear at
any given time and are therefore fatally non-unique. Finally, we propose an extension
of the solution concept to the multi-phase case which is at least guaranteed to
satisfy a weak-strong uniqueness principle.
acknowledgement: This project has received funding from the European Research Council
(ERC) under the European Union’s Horizon 2020 research and innovation programme
(grant agreement No 948819), and from the Deutsche Forschungsgemeinschaft (DFG,
German Research Foundation) under Germany’s Excellence Strategy – EXC-2047/1 – 390685813.
The content of this paper was developed and parts of it were written during a visit
of the first author to the Hausdorff Center of Mathematics (HCM), University of
Bonn. The hospitality and the support of HCM are gratefully acknowledged.
article_number: '2109.04233'
article_processing_charge: No
arxiv: 1
author:
- first_name: Sebastian
full_name: Hensel, Sebastian
id: 4D23B7DA-F248-11E8-B48F-1D18A9856A87
last_name: Hensel
orcid: 0000-0001-7252-8072
- first_name: Tim
full_name: Laux, Tim
last_name: Laux
citation:
ama: 'Hensel S, Laux T. A new varifold solution concept for mean curvature flow:
Convergence of the Allen-Cahn equation and weak-strong uniqueness. arXiv.
doi:10.48550/arXiv.2109.04233'
apa: 'Hensel, S., & Laux, T. (n.d.). A new varifold solution concept for mean
curvature flow: Convergence of the Allen-Cahn equation and weak-strong uniqueness.
arXiv. https://doi.org/10.48550/arXiv.2109.04233'
chicago: 'Hensel, Sebastian, and Tim Laux. “A New Varifold Solution Concept for
Mean Curvature Flow: Convergence of the Allen-Cahn Equation and Weak-Strong Uniqueness.”
ArXiv, n.d. https://doi.org/10.48550/arXiv.2109.04233.'
ieee: 'S. Hensel and T. Laux, “A new varifold solution concept for mean curvature
flow: Convergence of the Allen-Cahn equation and weak-strong uniqueness,” arXiv.
.'
ista: 'Hensel S, Laux T. A new varifold solution concept for mean curvature flow:
Convergence of the Allen-Cahn equation and weak-strong uniqueness. arXiv, 2109.04233.'
mla: 'Hensel, Sebastian, and Tim Laux. “A New Varifold Solution Concept for Mean
Curvature Flow: Convergence of the Allen-Cahn Equation and Weak-Strong Uniqueness.”
ArXiv, 2109.04233, doi:10.48550/arXiv.2109.04233.'
short: S. Hensel, T. Laux, ArXiv (n.d.).
date_created: 2021-09-13T12:17:10Z
date_published: 2021-09-09T00:00:00Z
date_updated: 2023-05-03T10:34:38Z
day: '09'
department:
- _id: JuFi
doi: 10.48550/arXiv.2109.04233
ec_funded: 1
external_id:
arxiv:
- '2109.04233'
keyword:
- Mean curvature flow
- gradient flows
- varifolds
- weak solutions
- weak-strong uniqueness
- calibrated geometry
- gradient-flow calibrations
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2109.04233
month: '09'
oa: 1
oa_version: Preprint
project:
- _id: 0aa76401-070f-11eb-9043-b5bb049fa26d
call_identifier: H2020
grant_number: '948819'
name: Bridging Scales in Random Materials
publication: arXiv
publication_status: submitted
status: public
title: 'A new varifold solution concept for mean curvature flow: Convergence of the
Allen-Cahn equation and weak-strong uniqueness'
type: preprint
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2021'
...